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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+showcase.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,115 @@
 ---
+language:
+- en
 license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/showcase.jpg"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
 ---
+# SDArt : Synesthesia (version based on 2.1 768px)
+
+![Showcase](https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/showcase.jpg)
+
+## Theme
+
+Dear @Challengers ,
+
+> In a world where colors have flavor, and sounds have texture, 
+> The senses intertwine to create a sensory rapture.
+> Welcome to “Synesthesia”- where the ordinary is extraordinary,
+> And art takes on a meaning that’s truly visionary! :Rainbowpink: 
+
+Synesthesia /ˌsɪn.əsˈθiː.ʒə/: an anomalous blending of the senses in which the stimulation of one modality simultaneously produces sensation in a different modality. Synesthetes hear colors, feel sounds, and taste shapes.
+
+* Create an image that captures the essence of synesthesia sensory! Explore the intersections of sound, color, and texture. What’s it like tasting a melody, feeling a scent, or seeing colored words?
+
+* What would it look it look like to taste the sound of a thunderstorm? How would you visualize the sensation of feeling the warmth of the sun on your skin? Or how about tasting a particular color–such as a bright red apple or a cool blueberry?
+
+* Explore the possibilities of synesthesia and its many interpretations!
+
+
+## Model description 
+
+This is a model related to the "Picture of the Week" contest on Stable Diffusion discord..
+
+I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "SDArt" and I balance the learning on the low side, so that it doesn't just replicate creations.
+
+The total dataset is made of 39 pictures. It was trained on [Stable diffusion 2.1 768px](https://huggingface.co/stabilityai/stable-diffusion-2-1). I used [EveryDream](https://github.com/victorchall/EveryDream2trainer) to do the training, 100 total repeat per picture. The pictures were tagged using the token "SDArt", and an arbitrary token I choose. The dataset is provided below, as well as a list of usernames and their corresponding token.
+
+The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
+
+## Trained tokens
+
+* SDArt
+* dyce
+* bnp
+* keel
+* aten
+* fcu
+* lpg
+* mth
+* elio
+* gani
+* pfa
+* kprc
+* cpec
+* kuro
+* asot
+* psst
+* sqm
+* irgc
+* cq
+* utm
+* guin
+* crit
+* mlas
+* isch
+* vedi
+* dds
+* acu
+* oxi
+* kohl
+* maar
+* mako
+* mds
+* mert
+* mgt
+* miki
+* minh
+* mohd
+* mss
+* muc
+* mwf
+
+## Download links
+
+[SafeTensors](https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/SDArt_synesthesia768.safetensors)
+
+[CKPT](https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/SDArt_synesthesia768.ckpt)
+
+[Config (yaml)](https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/SDArt_synesthesia768.yaml)
+
+[Dataset](https://huggingface.co/Guizmus/SDArt_synesthesia768/resolve/main/dataset.zip)
+
+## 🧨 Diffusers
+
+This model can be used just like any other Stable Diffusion model. For more information,
+please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
+
+You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "Guizmus/SDArt_synesthesia768"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "SDArt minh"
+image = pipe(prompt).images[0]
+
+image.save("./SDArt.png")
+```

SDArt_synesthesia768.ckpt

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SDArt_synesthesia768.safetensors

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SDArt_synesthesia768.yaml

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+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

dataset.zip

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model_index.json

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+{
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+  "_diffusers_version": "0.13.0",
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+  "scheduler": [
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+    "DDPMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+  "_diffusers_version": "0.13.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
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+  "clip_sample_range": 1.0,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

text_encoder/config.json

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+{
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+  "architectures": [
+    "CLIPTextModel"
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+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
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+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
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+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "vocab_size": 49408
+}

text_encoder/pytorch_model.bin

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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+  "add_prefix_space": false,
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+  "name_or_path": "F:\\AI\\Data\\Diffusers\\stable-diffusion-2-1",
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+}

unet/config.json

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+  "_diffusers_version": "0.13.0",
+  "_name_or_path": "F:\\AI\\Data\\Diffusers\\stable-diffusion-2-1",
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vae/config.json

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+  "_diffusers_version": "0.13.0",
+  "_name_or_path": "F:\\AI\\Data\\Diffusers\\stable-diffusion-2-1",
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